Creating meaningful word clouds!

This package is developed to serve as the one-step solution to create meaningful and visually appealing word clouds. To create meaningful word clouds, data scientists typically takes multiple steps to clean the data, such as removing stopwords, removing punctuation and digits, making the letters lower cases, conducting lemmatization and stemming. This package will help data scientists clean the data easily following the common practices and also allow to a meaningful word cloud with customized stopwords.

There are 4 functions in this package:

• CleverClean A preprocessor to convert all the letters to lower case and remove punctuations.

• CleverLemStem A preprocessor to conduct lemmatization and stemming on the text.

• CleverStopwords A comprehensive list of English stopwords that allow adding more customized words.

• CleverWordCloud As function to generate a meaningful word cloud that allows customized stopwords.

Packages that have similar functions:

• WordCloud: a word count generator that emphasis more frequently used words from an array of strings and represents them in the form of an image.

What we do differently:

• Our aim is to improve on the pre-processing of strings before creating a wordcloud in order to make it more user specific and efficient.

• Word cloud only eliminates limited amount of stopwords, but with our package we are giving users the opportunity to add more stopwords that cater to their analysis.

• We are focused on removing as many redundant and duplicate words by setting strings to lower case, removing punctuation, lemmatizing and stemming the text.

$ pip install clevercloud

clevercloud can be used to preprocess text and create a meaningful word cloud with customized stopwords as follows:

from clevercloud.CleverClean import CleverClean
from clevercloud.CleverLemStem import CleverLemStem
from clevercloud.CleverStopwords import CleverStopwords
from clevercloud.CleverWordCloud import CleverWordCloud

import pandas as pd
text = ["is is a feet feet crying beautiful123", "maximum feet RUNNING!!", "BEAUTIFUL feet beautiful crying"]
test_text = pd.Series(text) # input pandas series

clean_text = CleverClean(test_text)
final_text = CleverLemStem(clean_text)
new_stopwords = CleverStopwords({"foot", "cry"})
WordCloud = CleverWordCloud(final_text, new_stopwords, max_w=3)

Contributors of the project: Amelia Tang, Arushi Ahuja, Victor Francis, Adrianne Leung

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

clevercloud was created by Amelia Tang, Arushi Ahuja, Victor Francis, Adrianne Leung. It is licensed under the terms of the MIT license.

clevercloud was created with cookiecutter and the py-pkgs-cookiecutter template.